A system for monitoring vehicle traffic may include a camera positioned to capture images within a license plate detection zone, the images may represent license plates of vehicles. The system may include an electronic device identification sensor that detects and stores electronic device identifiers of electronic devices located within an electronic device detection zone, and a computing system that detects, using the images, a license plate ID of a vehicle, compare the license plate ID of the vehicle to a database of trusted vehicle license plate IDs, identifies the vehicle as a suspicious vehicle, the identification based at least in part on the comparison of the license plate ID of the vehicle to the database of trusted vehicle license plate IDs, and correlates the license plate ID of the vehicle with at least one of the plurality of stored electronic device identifiers.

A system for monitoring vehicle traffic may include a camera positioned to capture images within a license plate detection zone, the images may represent license plates of vehicles. The system may include an electronic device identification sensor that detects and stores electronic device identifiers of electronic devices located within an electronic device detection zone, and a computing system that detects, using the images, a license plate ID of a vehicle, compare the license plate ID of the vehicle to a database of trusted vehicle license plate IDs, identifies the vehicle as a suspicious vehicle, the identification based at least in part on the comparison of the license plate ID of the vehicle to the database of trusted vehicle license plate IDs, and correlates the license plate ID of the vehicle with at least one of the plurality of stored electronic device identifiers.

A cross-domain image comparison method and a cross-domain image comparison system are provided. The cross-domain image comparison method includes the following steps. Two videos in cross-domain are obtained. The videos are generated by different types of devices. A plurality of semantic segmentation areas are obtained from one frame of each of the videos. A region of interest pair (ROI pair) is obtained according to moving paths of the semantic segmentation areas in the videos. Two bounding boxes and two central points of the ROI pair are obtained. A similarity between the frames is obtained according to the bounding boxes and the central points.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a vehicle sensor object label, and a vehicle sensor confidence level for the vehicle sensor object label, by processing a vehicle sensor image with a neural network, determine a stationary sensor object label, and a stationary sensor confidence level for the stationary sensor object label, by processing a stationary sensor image with the neural network, and if the stationary sensor confidence level is greater than the vehicle sensor confidence level, operate a vehicle by determining a vehicle path based on the stationary sensor object label.

A surveillance apparatus (100) includes a detection unit (102) processing an image in which an area around a road is captured and detecting that the relative distance between a person and a vehicle included in the image is in a state of being equal to or less than a reference value, and an output unit (104) selecting a type of information to be output, by using an attribute of the person detected to be in the state, and outputting information of the selected type.

A surveillance apparatus (100) includes a detection unit (102) processing an image in which an area around a road is captured and detecting that the relative distance between a person and a vehicle included in the image is in a state of being equal to or less than a reference value, and an output unit (104) selecting a type of information to be output, by using an attribute of the person detected to be in the state, and outputting information of the selected type.

A number-of-occupants detection system (1) includes an image correction unit (120) that generates a corrected image based on an image generated by an imaging unit (110) that images a vehicle traveling on a road having a plurality of lanes by executing correction processing on the image for reducing blurring of a subject captured in the image using a parameter according to a position where the vehicle travels, and a count unit (130) that counts the number of occupants of the vehicle using the corrected image generated by the image correction unit (120).

A system for monitoring movement of vehicles in an arena. Imaging devices are positioned to capture images of vehicles present in the arena. Each path in which a vehicle can travel in the arena has an associated sequence of imaging devices I.sub.1, . . . I.sub.m-1, I.sub.m, . . . I.sub.n, such that a vehicle traveling along the path appears sequentially in the field of view of imaging devices I.sub.1, . . . I.sub.m-1, I.sub.m, . . . I.sub.n. For at least one path among the paths in the arena, there is no overlap in the fields of view of at least one pair of two consecutive imaging devices in the sequence of imaging devices associated with the path. A processor executes image analysis software and recursively identifies a vehicle in images obtained by the imaging devices in the sequence.

A system for monitoring movement of vehicles in an arena. Imaging devices are positioned to capture images of vehicles present in the arena. Each path in which a vehicle can travel in the arena has an associated sequence of imaging devices I.sub.1, . . . I.sub.m-1, I.sub.m, . . . I.sub.n, such that a vehicle traveling along the path appears sequentially in the field of view of imaging devices I.sub.1, . . . I.sub.m-1, I.sub.m, . . . I.sub.n. For at least one path among the paths in the arena, there is no overlap in the fields of view of at least one pair of two consecutive imaging devices in the sequence of imaging devices associated with the path. A processor executes image analysis software and recursively identifies a vehicle in images obtained by the imaging devices in the sequence.

Systems and methods are provided for the classification of anomalies present in road regions. Anomalies may be extrinsic or intrinsic. Intrinsic anomalies may pose a greater safety risk to drivers than extrinsic anomalies. Intrinsic anomalies may be identified by detection of an evasive maneuver of a vehicle in a road region, measurement of the properties of other vehicles and the surrounding environment in the road region, determination of a vehicle behavior pattern in a road region, and comparison of the determined vehicle behavior pattern in the road region to other vehicle behavior patterns stored in a database.